{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Data processing"
      ],
      "metadata": {
        "id": "vTPivpdOvmq4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkpCMaoRvlkq",
        "outputId": "4b10ae35-20fa-41f2-e35f-9fddda55de3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before data pre-process, Instances：10000，Features：14\n",
            "Index(['mental_health_history', 'seeks_treatment', 'physical_activity_days',\n",
            "       'depression_score', 'anxiety_score', 'social_support_score',\n",
            "       'productivity_score', 'mental_health_risk', 'total_mental_burden',\n",
            "       'energy_index', 'resilience', 'gender_Female', 'gender_Male',\n",
            "       'gender_Non-binary', 'gender_Prefer not to say',\n",
            "       'employment_status_Employed', 'employment_status_Self-employed',\n",
            "       'employment_status_Student', 'employment_status_Unemployed',\n",
            "       'work_environment_Hybrid', 'work_environment_On-site',\n",
            "       'work_environment_Remote', 'age_bin_10-19', 'age_bin_20-29',\n",
            "       'age_bin_30-39', 'age_bin_40-49', 'age_bin_50-59', 'age_bin_60-69',\n",
            "       'stress_level_bin_Low', 'stress_level_bin_Med', 'stress_level_bin_High',\n",
            "       'sleep_hours_bin_Very Short', 'sleep_hours_bin_Short',\n",
            "       'sleep_hours_bin_Normal', 'sleep_hours_bin_Long',\n",
            "       'gender_employment_Female_Employed',\n",
            "       'gender_employment_Female_Self-employed',\n",
            "       'gender_employment_Female_Student',\n",
            "       'gender_employment_Female_Unemployed',\n",
            "       'gender_employment_Male_Employed',\n",
            "       'gender_employment_Male_Self-employed',\n",
            "       'gender_employment_Male_Student', 'gender_employment_Male_Unemployed',\n",
            "       'gender_employment_Non-binary_Employed',\n",
            "       'gender_employment_Non-binary_Self-employed',\n",
            "       'gender_employment_Non-binary_Student',\n",
            "       'gender_employment_Non-binary_Unemployed',\n",
            "       'gender_employment_Prefer not to say_Employed',\n",
            "       'gender_employment_Prefer not to say_Self-employed',\n",
            "       'gender_employment_Prefer not to say_Student',\n",
            "       'gender_employment_Prefer not to say_Unemployed'],\n",
            "      dtype='object')\n",
            " Data processing completed，Instances：5000，Features：51\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-3f37502c033d>:16: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df.replace(binary_map, inplace=True)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# 读取数据\n",
        "df = pd.read_csv(\"mental_health_dataset.csv\")\n",
        "\n",
        "# 输出现在特征数量\n",
        "print(f\"Before data pre-process, Instances：{df.shape[0]}，Features：{df.shape[1]}\")\n",
        "\n",
        "# 二值字段编码\n",
        "binary_map = {\n",
        "    'seeks_treatment': {'Yes': 1, 'No': 0},\n",
        "    'mental_health_history': {'Yes': 1, 'No': 0},\n",
        "}\n",
        "df.replace(binary_map, inplace=True)\n",
        "\n",
        "\n",
        "# 分箱处理\n",
        "df['age_bin'] = pd.cut(df['age'], bins=list(range(10, 71, 10)), labels=['10-19', '20-29', '30-39', '40-49', '50-59', '60-69'])\n",
        "df['stress_level_bin'] = pd.cut(df['stress_level'], bins=[0, 4, 7, 10], labels=['Low', 'Med', 'High'])\n",
        "df['sleep_hours_bin'] = pd.cut(df['sleep_hours'], bins=[0, 4, 6, 8, 10], labels=['Very Short', 'Short', 'Normal', 'Long'])\n",
        "\n",
        "\n",
        "# 构造交叉组合特征\n",
        "df['gender_employment'] = df['gender'] + '_' + df['employment_status']\n",
        "# 构造组合特征\n",
        "df['total_mental_burden'] = df['depression_score'] + df['anxiety_score'] # 整体心理负担程度\n",
        "df['energy_index'] = df['physical_activity_days'] * df['productivity_score'] # 活力指数\n",
        "df['resilience'] = df['social_support_score'] / (df['depression_score'] + 1) # 抗压能力指数\n",
        "\n",
        "\n",
        "# 去掉原始特征\n",
        "df.drop(columns=['age', 'stress_level', 'sleep_hours'], inplace=True)\n",
        "# df.drop(columns=['depression_score', 'anxiety_score', 'physical_activity_days', 'productivity_score', 'social_support_score'], inplace=True)\n",
        "\n",
        "\n",
        "# One-hot 编码\n",
        "df = pd.get_dummies(df, columns=[\n",
        "    'gender', 'employment_status', 'work_environment', 'age_bin', 'stress_level_bin', 'sleep_hours_bin', 'gender_employment'\n",
        "])\n",
        "\n",
        "\n",
        "# 结果\n",
        "# print(df.head())\n",
        "df = df.iloc[:5000]\n",
        "print(df.columns)\n",
        "print(f\" Data processing completed，Instances：{df.shape[0]}，Features：{df.shape[1]}\")\n",
        "\n",
        "\n",
        "df.to_csv(\"processed_depression_data.csv\", index=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training and results"
      ],
      "metadata": {
        "id": "NmiCGyMcSyDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold, learning_curve\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import (\n",
        "    precision_score, recall_score, accuracy_score, f1_score,\n",
        "    mean_squared_error, mean_absolute_error, confusion_matrix\n",
        ")\n",
        "import seaborn as sns\n",
        "from sklearn.base import clone\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "df = pd.read_csv(\"processed_depression_data.csv\")\n",
        "X = df.drop(columns=['mental_health_risk'])\n",
        "\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(df['mental_health_risk'])  # y 编码为[0, 1, 2]\n",
        "\n",
        "outer_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "inner_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
        "\n",
        "models = {\n",
        "    'Naive Bayes': {\n",
        "        'model': GaussianNB(),\n",
        "        'params': {'var_smoothing': [1e-5, 1e-1, 1]}  # 感觉意义不大\n",
        "    },\n",
        "    # 'Logistic Regression': {\n",
        "    #     'model': LogisticRegression(max_iter=1000, random_state=42),\n",
        "    #     'params': {'C': [50, 100, 500], 'penalty': ['l2'], 'solver': ['lbfgs', 'liblinear']}, # L2：模型保留所有特征但防止过拟合\n",
        "    # },\n",
        "    'SVM': {\n",
        "        'model': SVC(random_state=42),\n",
        "        'params': {'C': [50, 100, 500], 'kernel': ['rbf'], 'gamma': ['scale']},\n",
        "    },\n",
        "    'MLP': {\n",
        "        'model': MLPClassifier(max_iter=1000, random_state=42, ), # verbose=True),\n",
        "        'params': {\n",
        "            'hidden_layer_sizes': [(4,), (8,), (4, 8), (16,), (8, 16)], #  (64,), (128,), (64, 32), (128,64), (128, 64, 32)\n",
        "            'alpha': [0.0001, 0.001, 0.1, 1, 10], # 0.0001默认值\n",
        "            #'activation': ['tanh']\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "results = []\n",
        "\n",
        "\n",
        "for name, config in models.items():\n",
        "    print(f\"Training and evaluating model: {name}\")\n",
        "\n",
        "    steps = [('scaler', StandardScaler()), ('clf', config['model'])]\n",
        "\n",
        "    pipe = Pipeline(steps)\n",
        "    param_grid = {f'clf__{k}': v for k, v in config['params'].items()}\n",
        "\n",
        "    f1_scores = []\n",
        "    precisions = []\n",
        "    recalls = []\n",
        "    accuracies = []\n",
        "    rmses = []\n",
        "    maes = []\n",
        "    all_conf_matrices = []\n",
        "\n",
        "    for train_idx, test_idx in outer_cv.split(X, y):\n",
        "        X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
        "        y_train, y_test = y[train_idx], y[test_idx]\n",
        "\n",
        "        grid = GridSearchCV(pipe, param_grid, cv=inner_cv, scoring='f1_macro', n_jobs=-1)\n",
        "        grid.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "        # 保存本次 GridSearch 的所有参数组合与评分结果\n",
        "        # cv_results_df = pd.DataFrame(grid.cv_results_)\n",
        "        # cv_results_df.to_csv(\n",
        "        #     f\"results/hyperparameter_tuning/{name.replace(' ', '_')}_fold_{len(f1_scores)}.csv\", index=False\n",
        "        # )\n",
        "\n",
        "        # 打印本轮调参找到的最佳参数\n",
        "        print(f\"Best params for fold: {grid.best_params_}\")\n",
        "\n",
        "        best_model = grid.best_estimator_\n",
        "        y_pred = best_model.predict(X_test)\n",
        "\n",
        "        # 分类报告\n",
        "        # print(f\"Classification report for fold {len(f1_scores)} ({name}):\")\n",
        "        # print(classification_report(y_test, y_pred, target_names=le.classes_))\n",
        "\n",
        "\n",
        "        f1_scores.append(f1_score(y_test, y_pred, average='macro'))\n",
        "        precisions.append(precision_score(y_test, y_pred, average='macro'))\n",
        "        recalls.append(recall_score(y_test, y_pred, average='macro'))\n",
        "        accuracies.append(accuracy_score(y_test, y_pred))\n",
        "        all_conf_matrices.append(confusion_matrix(y_test, y_pred, labels=range(len(le.classes_))))\n",
        "\n",
        "\n",
        "\n",
        "    results.append({\n",
        "        'Model': name,\n",
        "        'Accuracy': np.mean(accuracies),\n",
        "        'Precision': np.mean(precisions),\n",
        "        'Recall': np.mean(recalls),\n",
        "        'F1-score': np.mean(f1_scores),\n",
        "    })\n",
        "\n",
        "\n",
        "    #  平均混淆矩阵展示\n",
        "    avg_conf_matrix = np.mean(all_conf_matrices, axis=0)\n",
        "    plt.figure()\n",
        "    sns.heatmap(avg_conf_matrix, annot=True, fmt=\".1f\", cmap='Blues',\n",
        "            xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "    plt.title(f\"Confusion Matrix: {name}\")\n",
        "    plt.xlabel(\"Predicted Label\")\n",
        "    plt.ylabel(\"True Label\")\n",
        "    plt.tight_layout()\n",
        "    # 保存混淆矩阵图\n",
        "    # plt.savefig(f\"results/confusion_matrices/{name}_confusion_matrix.png\")\n",
        "    plt.show()\n",
        "    # plt.close(\"all\")\n",
        "\n",
        "\n",
        "# 综合模型评估结果表格保存为csv\n",
        "results_df = pd.DataFrame(results).sort_values(by='F1-score', ascending=False)\n",
        "# results_df.to_csv(\"results/model_comparison.csv\", index=False)\n",
        "print(\"All Models Comparison：\")\n",
        "print(results_df.round(4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "G6RsYXCTSxsG",
        "outputId": "89a4e3ab-5fd2-40be-c057-641d6cb23368"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training and evaluating model: Naive Bayes\n",
            "Best params for fold: {'clf__var_smoothing': 1}\n",
            "Best params for fold: {'clf__var_smoothing': 0.1}\n",
            "Best params for fold: {'clf__var_smoothing': 0.1}\n",
            "Best params for fold: {'clf__var_smoothing': 0.1}\n",
            "Best params for fold: {'clf__var_smoothing': 0.1}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training and evaluating model: SVM\n",
            "Best params for fold: {'clf__C': 100, 'clf__gamma': 'scale', 'clf__kernel': 'rbf'}\n",
            "Best params for fold: {'clf__C': 100, 'clf__gamma': 'scale', 'clf__kernel': 'rbf'}\n",
            "Best params for fold: {'clf__C': 50, 'clf__gamma': 'scale', 'clf__kernel': 'rbf'}\n",
            "Best params for fold: {'clf__C': 50, 'clf__gamma': 'scale', 'clf__kernel': 'rbf'}\n",
            "Best params for fold: {'clf__C': 500, 'clf__gamma': 'scale', 'clf__kernel': 'rbf'}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training and evaluating model: MLP\n",
            "Best params for fold: {'clf__alpha': 1, 'clf__hidden_layer_sizes': (4, 8)}\n",
            "Best params for fold: {'clf__alpha': 0.001, 'clf__hidden_layer_sizes': (4, 8)}\n",
            "Best params for fold: {'clf__alpha': 1, 'clf__hidden_layer_sizes': (4, 8)}\n",
            "Best params for fold: {'clf__alpha': 1, 'clf__hidden_layer_sizes': (4, 8)}\n",
            "Best params for fold: {'clf__alpha': 0.0001, 'clf__hidden_layer_sizes': (4,)}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All Models Comparison：\n",
            "         Model  Accuracy  Precision  Recall  F1-score\n",
            "2          MLP    0.9942     0.9932  0.9935    0.9934\n",
            "1          SVM    0.9418     0.9380  0.9309    0.9343\n",
            "0  Naive Bayes    0.8522     0.8306  0.8700    0.8442\n"
          ]
        }
      ]
    }
  ]
}